Prediction method and system for remaining service life of nonlinear degradation equipment

Abstract

The invention discloses a prediction method and system for the remaining service life of nonlinear degradation equipment. The method comprises the steps that according to time uncertainty parameters,individual difference parameters and measurement uncertainty parameters of the nonlinear degradation equipment, a nonlinear degradation equipment potential degradation model is established; sampling data of all the nonlinear degradation equipment at different moments is acquired; according to the sampling data of all the nonlinear degradation equipment at different moments, parameters of the nonlinear degradation equipment potential degradation model are determined; according to the nonlinear degradation equipment potential degradation model with the determined parameters, a remaining servicelife prediction model is determined; according to the remaining service life prediction model, a remaining service life value of the nonlinear degradation equipment is determined. According to the prediction method and system, the time uncertainty, individual difference and measurement uncertainty of the nonlinear degradation equipment are comprehensively considered, then the nonlinear degradationequipment potential degradation model is established, the remaining service life of the nonlinear degradation equipment is predicted, and then the accuracy of service life prediction is improved.

Description

technical field [0001] The invention relates to the technical field of remaining life prediction, in particular to a method and system for predicting the remaining life of non-linear degradation equipment. Background technique [0002] With the development of information and monitoring technology, the monitoring means of system degradation state has become more and more mature, and the remaining life prediction based on the state monitoring information has developed rapidly in the past few decades. However, the degradation of a large number of engineering equipment occurs randomly, such as the growth of metal fatigue cracks, the drift of gyroscopes, and the degradation of battery capacity. Therefore, it is difficult to predict the remaining life of such stochastically degraded equipment in a deterministic way, and the probability density function for evaluating the remaining life of equipment through degradation monitoring information can not only incorporate the considerati...

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Problems solved by technology

[0003] The current remaining life prediction of non-linear stochastic degradation equipment using monitoring information can only partially reflect the potential degradation state, and does not consider time uncertainty parameters, individual difference parameters and measurement uncertainty parameters. The remaining life prediction of stochastic degradation equipment has the problem of low prediction accuracy

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